Customer Research Survey (Enterprises and Businesses)

We’re building a new kind of data platform where AI/ML enterprise data needs are met through real human contributors. Tell us what matters most to your organization.

We're building a trusted data marketplace where enterprises can source high-quality, compliant data from verified providers. Help shape the future of ethical data acquisition.
Welcome and thank you for participating in our survey.

We're developing a pioneering data marketplace that enables enterprises to access ethically sourced, high-quality data through a transparent and trusted ecosystem. Our platform connects businesses seeking reliable data with individuals and communities who are fairly compensated for their data contributions—creating value for all stakeholders while maintaining the highest ethical standards.

Our mission is to transform the data economy by building a trusted infrastructure where enterprises can confidently source the data they need for AI development, analytics, and innovation, while ensuring data providers maintain control over how their information is used. We envision a marketplace where transparency, quality, and ethical sourcing drive competitive advantage.

This survey will help us understand your organization's data needs, current sourcing challenges, and interest in accessing data through a managed trust platform that prioritizes quality, compliance, and ethical standards.

Your insights will directly influence how we build this platform to serve enterprise needs. We value your expertise and time. Organizations that complete the survey may request early access to our platform beta and receive our industry report on ethical data sourcing trends.

Please note: This survey is for market research purposes only. All responses are confidential and will be analyzed in aggregate. Participation is voluntary. By completing this survey, you agree to have your responses used for B2B market research. For questions or concerns, please contact info@jalia.tech .

Survey length: Approximately 10-15 minutes

[For languages other than English, please use the translation feature in your browser settings.]

Section 1: Organization Profile
1. Which departments in your organization are involved in making decisions about data acquisition (budget, vendor selection, approval) for AI/ML product development? (Select all that apply)

Other
Which departments in your organization actively use external data or develop AI/ML products and solutions? (Select all that apply)

Other
2. Which industry sector(s) best describe your organization? (Select all that apply)

Other
3. How many employees does your team or organization have?
4. Who has the final approval authority for external data acquisition in your organization?

Other
Section 2: Current Data Practices
5. What are your organization's primary data sources? (Select all that apply)

Other
6. Do you currently work with any vendors that provide pre-licensed or consent-based human data (e.g., voice, behavioral, text, image data)? (Please share their names and particular forms of data)
7. Do your products/services rely on data that reflects real-world human behavior, expression, or identity (e.g., speech, text, biometric, social)?

Other
8. How frequently does your organization seek to acquire external data?

Other
9. What is your organization's approximate annual budget for external data acquisition?
10. Which of the following are data challenges for your organization? (Select all that apply)

Other
Section 3: Data Needs and Use Cases
11. Which types of data are most valuable to your organization? (Select all that apply)

Other
12. For what use cases does your organization require external data that may be challenging to collect in-house (Select all that apply)

Other
13. What data-powered products or services does your organization produce? (Select all that apply)

Other
14. Does your organization currently use synthetic data in AI/ML development?

Other
15. For what use cases is your organization most likely to use synthetic data? (Select all that apply)

Other
16. What do you see as the primary advantages of synthetic data for your workflows?(Rank the top 3)
17. What challenges or limitations has your organization faced with synthetic data?(Select all that apply)
18. Have you encountered challenges where synthetic data failed to adequately substitute for real-world human data? What would make a real human dataset more valuable than synthetic data for your use case?
19. Would your organization consider complementing synthetic datasets with ethically-sourced, real human data under a licensing or leasing model?
Section 4: Ethical Considerations and Requirements
20. Does your organization have formal policies for ethical data sourcing and usage?
21. Are you currently required to meet any external certifications, standards, or regulations related to data ethics (e.g., GDPR, AI Act, ISO, DEI initiatives)?

22. Which ethical considerations are most important when sourcing external data? (Select all that apply)

Rows	1 = Not Important	2 = Somewhat Important	3 = Important	4 = Very Important	5 = Extremely Important
Reducing downstream AI bias risk
User consent transparency
Fair compensation to data providers
Data privacy protection
Bias prevention in datasets
Clear data usage limitations
Right to data deletion
Data security measures
Regulatory compliance
Explicit, revocable user consent
Representational equity across populations
Fair compensation to data contributors
Environmental sustainability of data sourcing (optional, future-looking)

Section 5: Willingness to Engage with Data Trusts
23. Are you familiar with data trusts and data cooperatives as data sourcing models?
24. How much MORE would you be willing to pay for ethically sourced data with transparent consent compared to current sources?
25. Which factors would most influence your decision to use a data trust platform? (Select all that apply)

Other
18. What concerns might prevent you from using a data trust model? (Select all that apply)

Other
18. Would a leasing model (vs. purchasing) for ethically sourced data better suit your needs?
Section 6: Technical Requirements
26. Which data modalities are most important to your work?

Other
27. What data formats does your organization prefer? (Select all that apply)

Other
28. Do you currently use or would you be interested in bespoke datasets based on market trends or product innovation advancement?
Section 7: Ethically-Aligned Data Certification
Context:

Some organizations are beginning to explore the idea of certifying datasets as “ethically sourced” or “fair-use compliant”—similar to how certain goods (like coffee or produce) are labeled “fair trade” or “cruelty-free.” These certifications aim to increase trust, signal responsible AI development, and reduce regulatory risk.

Consider this Scenario:

Imagine your organization could certify either (a) the datasets you already use, or (b) the datasets you acquire from third parties. Certification would provide transparency into sourcing practices, user consent, and population representativeness. This label could be used for internal risk review, client assurance, or even public-facing trust indicators.

29. How valuable would the following benefits of data certification be to your organization?

Rows	1 = Not valuable	2 = Somewhat Valuable	3 - Valuable	4 - Very Valuable	5 - 5 = Extremely valuable
Helping clients trust your AI/ML systems
Supporting compliance with emerging data laws
Internal audit and documentation of data sourcing
Marketing, brand trust, or brand differentiation
Building internal accountability standards
Vendor and data services transparency
Reducing downstream AI bias risk

30. How useful would it be if third-party service could audit and certify your existing datasets for ethical sourcing, representational fairness, and potential adverse impacts?
31. What would you expect to be included in a credible data certification process? E.g., documentation of consent, demographic transparency, dataset usage logs, etc.
32. What concerns or challenges would prevent your organization from pursuing dataset certification?

Other
33. What challenges has your organization experienced when working with external data vendors? (Select all that apply)
Section 8: Closing Insights
34. What is your role within your organization as it relates to data use, management, or product development? (Select all that apply)
Country
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We’re building a new kind of data platform where AI/ML enterprise data needs are met through real human contributors. Tell us what matters most to your organization.

Section 1: Organization Profile

Section 2: Current Data Practices

Section 3: Data Needs and Use Cases

Section 4: Ethical Considerations and Requirements

Section 5: Willingness to Engage with Data Trusts

Section 6: Technical Requirements

Section 7: Ethically-Aligned Data Certification

Section 8: Closing Insights